Graph and download economic data for Unemployment Rate - With a Disability, 16 Years and over (LNU04074597) from Jun 2008 to Jun 2025 about disability, 16 years +, household survey, unemployment, rate, and USA.

In 2023, there were 7.53 million persons with a disability employed either full-time or part-time in the United States. This was an increase from the previous year, when 6.96 million persons with a disability were employed. The increase in employment among persons with disabilities may be due to the recovery of the COVID-19 pandemic. The persons with a disability section of the Current Population Survey, (CPS) is a set of six questions to identify persons have physical, mental, or emotional conditions that cause serious difficulty with their daily activities.

In the United States, the employment rate of men with a disability was 24.8 percent in 2023. This was an increase from the previous year, when 23.5 percent of men with a disability were employed. This increase in employment among persons with disabilities may be due to the recovery of the COVID-19 pandemic that has affected everyone's employment, as can be seen in the employment rate of adults in the United States. The persons with a disability section of the Current Population Survey (CPS) is a set of six questions to identify persons have physical, mental, or emotional conditions that cause serious difficulty with their daily activities.

In the United States, the employment rate of persons with a disability was 22.7 percent in 2024. This was an increase from the previous year, when 22.5 percent of persons with a disability were employed. The persons with a disability section of the Current Population Survey (CPS) is a set of six questions to identify persons have physical, mental, or emotional conditions that cause serious difficulty with their daily activities.

In the United States, the employment rate of women with a disability was 20.5 percent in 2023. This was an increase from the previous year, when 19.4 percent of women with a disability were employed. This increase in employment among persons with disabilities may be due to the recovery of the COVID-19 pandemic that has affected everyone's employment, as can be seen in the employment rate of adults in the United States. The employment rate of persons with a disability for both genders can be found here. The persons with a disability section of the Current Population Survey (CPS) is a set of six questions to identify persons have physical, mental, or emotional conditions that cause serious difficulty with their daily activities.

In 2024, the unemployment rate of people living with mental disabilities in South Korea rose to *** percent, representing an increase from the previous year. Only ** percent of people with mental disabilities were part of the economically active population that year.

IntroductionAutism spectrum disorder is a lifelong neurodevelopmental disorder. The profile of functioning in autistic people is very heterogeneous, and it is necessary to take into account individual characteristics to better support integration in the workplace. However, unemployment rates are higher for autistic people than for other types of disabilities. We present a prospective case series to explore the feasibility and efficacy of an individual-supported program to enhance placement in a sheltered work environment delivered by an Italian community day care center.MethodsAutistic subjects, aged from 12 to 31 years, participated in an individual-supported program regarding employment in sheltered art workshops, integrated into the regular activity of a semi-residential center three times a week for 1 year. Their feasibility retention rate and time worked per session were registered; moreover, working methods efficacy and self-organization improvement were tracked by the Likert-based rating system. Secondary outcome measures span functional levels, challenge behaviors, and sensory problems.ResultsAll the individuals presented a good adaptation to the environment, with a significant increase in time worked per session. After 1 year, the intervention allowed an increase in tasks completed in an assigned complex job and an improvement in self-organization within the work schedule in a group of subjects consisting mainly of severe-to-moderate levels of autism severity (86.6%). Finally, we observed a significant increase in independent functioning areas of the TEACCH transitional assessment profile. Challenge behaviors and sensory problems were also recorded.ConclusionThis case series supports the idea that individual-supported programs for placement in sheltered job environments delivered by community day care centers could be feasible and effective for ASD with higher levels of severity and co-occurring intellectual disability. Further targeted studies based on community models and accessible methods need to be planned to define the effectiveness of the intervention and promote improved practice at the community level with a better social impact.

Ukraine Unemployment: Layoffs Caused by State of Health, Old Age Pension and Disability data was reported at 2.000 % in 2017. This records an increase from the previous number of 1.800 % for 2016. Ukraine Unemployment: Layoffs Caused by State of Health, Old Age Pension and Disability data is updated yearly, averaging 1.150 % from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 2.000 % in 2017 and a record low of 0.700 % in 2009. Ukraine Unemployment: Layoffs Caused by State of Health, Old Age Pension and Disability data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.G012: Unemployment.

Goal 8Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allTarget 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countriesIndicator 8.1.1: Annual growth rate of real GDP per capitaNY_GDP_PCAP: Annual growth rate of real GDP per capita (%)Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high-value added and labour-intensive sectorsIndicator 8.2.1: Annual growth rate of real GDP per employed personSL_EMP_PCAP: Annual growth rate of real GDP per employed person (%)Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro-, small- and medium-sized enterprises, including through access to financial servicesIndicator 8.3.1: Proportion of informal employment in total employment, by sector and sexSL_ISV_IFEM: Proportion of informal employment, by sector and sex (ILO harmonized estimates) (%)Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the leadIndicator 8.4.1: Material footprint, material footprint per capita, and material footprint per GDPEN_MAT_FTPRPG: Material footprint per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollar)EN_MAT_FTPRPC: Material footprint per capita, by type of raw material (tonnes)EN_MAT_FTPRTN: Material footprint, by type of raw material (tonnes)Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDPEN_MAT_DOMCMPT: Domestic material consumption, by type of raw material (tonnes)EN_MAT_DOMCMPG: Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollars)EN_MAT_DOMCMPC: Domestic material consumption per capita, by type of raw material (tonnes)Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal valueIndicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilitiesSL_EMP_EARN: Average hourly earnings of employees by sex and occupation (local currency)Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilitiesSL_TLF_UEM: Unemployment rate, by sex and age (%)SL_TLF_UEMDIS: Unemployment rate, by sex and disability (%)Target 8.6: By 2020, substantially reduce the proportion of youth not in employment, education or trainingIndicator 8.6.1: Proportion of youth (aged 15–24 years) not in education, employment or trainingSL_TLF_NEET: Proportion of youth not in education, employment or training, by sex and age (%)Target 8.7: Take immediate and effective measures to eradicate forced labour, end modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labour, including recruitment and use of child soldiers, and by 2025 end child labour in all its formsIndicator 8.7.1: Proportion and number of children aged 5–17 years engaged in child labour, by sex and ageSL_TLF_CHLDEC: Proportion of children engaged in economic activity and household chores, by sex and age (%)SL_TLF_CHLDEA: Proportion of children engaged in economic activity, by sex and age (%)Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employmentIndicator 8.8.1: Fatal and non-fatal occupational injuries per 100,000 workers, by sex and migrant statusSL_EMP_FTLINJUR: Fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)SL_EMP_INJUR: Non-fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)Indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant statusSL_LBR_NTLCPL: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislationTarget 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and productsIndicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rateST_GDP_ZS: Tourism direct GDP as a proportion of total GDP (%)Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for allIndicator 8.10.1: (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adultsFB_ATM_TOTL: Number of automated teller machines (ATMs) per 100,000 adultsFB_CBK_BRCH: Number of commercial bank branches per 100,000 adultsIndicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service providerFB_BNK_ACCSS: Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider, by sex (% of adults aged 15 years and older)Target 8.a: Increase Aid for Trade support for developing countries, in particular least developed countries, including through the Enhanced Integrated Framework for Trade-related Technical Assistance to Least Developed CountriesIndicator 8.a.1: Aid for Trade commitments and disbursementsDC_TOF_TRDCMDL: Total official flows (commitments) for Aid for Trade, by donor countries (millions of constant 2018 United States dollars)DC_TOF_TRDDBMDL: Total official flows (disbursement) for Aid for Trade, by donor countries (millions of constant 2018 United States dollars)DC_TOF_TRDDBML: Total official flows (disbursement) for Aid for Trade, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_TRDCML: Total official flows (commitments) for Aid for Trade, by recipient countries (millions of constant 2018 United States dollars)Target 8.b: By 2020, develop and operationalize a global strategy for youth employment and implement the Global Jobs Pact of the International Labour OrganizationIndicator 8.b.1: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategySL_CPA_YEMP: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy

The table shows the number of people receiving social security benefits. These persons can live both in the Netherlands and abroad. These are persons receiving benefits for incapacity for work, unemployment, old age, social assistance and social assistance-related benefits. Persons receiving disability, unemployment, social assistance and assistance-related benefits will be available from 2007. The number of people receiving an old-age benefit has been included in the table since 2013. It is possible for a person to claim multiple benefits. These may be benefits of the same type (e.g. two disability benefits: WIA, WAZ, Wajong or WAO) or benefits of different types (such as benefits under the Unemployment Act (WW) and social assistance benefits). In the latter case, the person is included in both types of benefits. In the first case, only once (in the case of invalidity benefits). As of October 2021, there has been an increase in the number of WGA benefits. The reason for this is a quality improvement of the process so that a group of self-risk carriers that were previously missing are now included. This is not an increase in the regular number of WGA benefits, but an increase in "persons with a WGA benefit". In the total counts, the person is of course only counted once.

The compilation of data for StatLine tables that contain breakdowns by personal characteristics is always based on the most recent data from the Basic Registration of Persons (BRP). Because different StatLine tables are updated at different times, it may happen that a different version of the BRP is used for one table than for another table. This may result in limited differences compared to other tables with the same population. In this case, the most recent published figures are the most accurate. The figures refer to the last day of the reporting month.

Status of figures: The figures can be both provisional and definitive. The monthly figures are end-of-year figures. After one to two years, the figures become final.

Changes as of 29 March 2024: Added are: Further preliminary figures from July to September 2023.

When will there be new figures? New figures will come in July 2024.

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca

Introduction: Persons with mental illness experience social life restriction and stigma that may have implications for their work ability. The aims of this study are (i) to report experienced and anticipated discrimination and social functioning in persons with mental disabilities in Kenya and (ii) to investigate the association between experienced and anticipated discrimination, social functioning, and employment in this population.Materials and Methods: Cross-sectional study design where we randomly recruited 72 persons with mental illness through two networks of persons with psychosocial disabilities in Kenya. Experienced and anticipated discrimination were measured using the Discrimination and Stigma Scale version 12 (DISC 12) while social functioning was measured using the Social Functioning questionnaire (SFQ).Results: Experienced discrimination was reported by 81.9% in making or keeping friends, 69.7 and 56.3% in finding or keeping job, respectively, and 63.3% in dating or having an intimate relationship. Anticipated discrimination stopped 59.2% from applying for work, 40.8% from applying for education or training courses, and 63.4% from having a close personal relationship. Females reported an overall higher experienced discrimination than males. Unemployed participants had slightly increased rates of experienced and anticipated discrimination (9.5 vs. 9.1 and 2.5 vs. 2.3, respectively) (p > 0.05), while there was a significant association between impaired social functioning and unemployment [14.0 vs. 11.2 (p = 0.037)].Conclusion: The rates of experienced and anticipated discrimination faced by persons with mental disabilities in Kenya is high and with significant gender disparity. Although no strong associations were observed between experienced and anticipated discrimination and employment, impaired social functioning of persons with mental disabilities seems to have implications for employment. Further research is essential to understand the predictors of the discrimination and measures to reduce them in persons with psychosocial disabilities.

In 2024, there were around ******* people with severe disabilities who were unemployed in Germany. This was an increase from the previous year, when there were ******* unemployed. Figures peaked in 2014 at around *******.

This table contains figures on the benefit position of young people aged 15 to 27 who were resident in the Netherlands on 31 December of the reference year by gender, age, migration background and living situation. The benefit position of young people is divided into whether or not they receive benefits. The types of benefit are also included in relation to unemployment, disability and social assistance and social assistance-related benefits. The total number of young people on benefits in this table is lower than in regular StatLine tables on people on benefits because young people who do not live in the Netherlands or whose place of residence is unknown are not included in this table. The number of young people receiving benefits in this table is also lower than in the table Labor market situation for young people (15 to 27 years); region 2018. This is mainly because an exact reference date has been chosen in this table (31 December) while in the table Labor market situation young people (15 to 27 years); In the 2018 region, it was checked whether someone received a benefit somewhere in the month of October. See paragraph 3 for the above tables. A young person can claim several benefits. These can be benefits of the same type, for example two benefits in the context of disability, or benefits of different types, such as one benefit in the context of unemployment and social assistance. In the latter case, the young person is included in both types of benefits. In the first case, only once for disability benefits. In the total counts, the person is counted once. As a result, the sum of the categories is not equal to the total number of young people on benefits. Data available from: 2007 Status of the figures: These are definitive figures. Changes as of July 29, 2022: The figures for 2021 have been added. Changes as of December 8, 2021: The figures for 2020 have been added. To mitigate the consequences of the corona crisis, the Temporary bridging scheme for self-employed entrepreneurs (Tozo) has been in force since 1 March 2020. The Temporary Bridging Scheme for Self-Employed Entrepreneurs (Tozo) provides independent entrepreneurs with an additional payment for living expenses or a loan for working capital to deal with liquidity problems as a result of the corona crisis. Self-employed entrepreneurs can receive an amount from this scheme to supplement their income up to the social minimum. The first part of the Tozo expired at the end of May 2020, but due to an extension, the scheme was still in force at the end of 2020. The Tozo is regarded as a social assistance-related benefit, which means that the number of people receiving a social assistance benefit increased sharply in 2020. Changes as of December 13, 2019: The figures for 2018 have been added. For 2016, an earlier version incorrectly included young people who had died on December 31 of the year. As a result, the figures on the total number of young people and young people without social security benefits have been corrected. For 2017, the figures for AO benefits for the months of January to March were based on estimates. These were corrected after delivery of the correct sender files. Furthermore, the underlying codes of the classifications used in this table have been adjusted. These are now in line with the standard coding established by Statistics Netherlands. The structure of the table has not been changed. Changes as of October 30, 2018: For 2017, the figures of young people aged 15 to 27 for the benefit positions 'Unemployment', 'Social assistance and social assistance related', 'Incapacity for work; incl. Wajong', 'Wajong' and 'No benefit' corrected. The numbers were interchanged. The same applies to the figures of young people aged 15 to 27 (relatively). Changes as of 7 May 2018: Due to an improvement in the method, the figures of young people receiving disability benefits have been recalculated for the years 2013 to 2015. In addition, from 2015, the figures of the new Wajong 2015 scheme have been included. The revision has no consequences for the other figures of young people on benefits, but it does affect the total number of young people on benefits. Figures for 2016 have also been added. When will new numbers come out? The figures for 2022 will be published in the spring of 2023.

The COVID-19 pandemic heightened the focus on health and social services, benefiting the industry through increased government funding and overall demand. However, subsequent to the pandemic, particularly through 2023-24, the Personal Welfare Services industry encountered mounting pressures as economic strains drove more individuals and families to seek support. Despite rising demand, government funding tightened sharply under new fiscal constraints, particularly impacting broad-based community services. While targeted areas like family violence prevention received increased backing, eligibility rules for housing support became stricter, limiting access for many. With more organisations competing for shrinking pools of government and philanthropic funding, the industry has faced growing uncertainty and must adapt to continued funding volatility even as the need for its core services remains elevated. As a result, revenue for the Personal Welfare Services industry is expected to drop at an annualised 1.3% over the five years through 2025-26, to total $2.5 billion.Ongoing issues like a marked increase in substance use have amplified the need for personal welfare services, particularly in drug rehabilitation and counselling. Although inflation and cost-of-living pressures were severe from 2022 to early 2024, recent easing has provided some relief. However, the lingering impacts still weigh heavily on children and vulnerable groups, whose hardship has been exacerbated by reduced welfare support.Looking ahead, personal welfare services will face a more selective funding environment shaped by fiscal restraint and intensified competition. Government spending is expected to prioritise targeted programs, particularly in mental health and family violence prevention, while general community support services risk stagnating or losing ground if they cannot demonstrate clear results. With the population ageing and public funds set to be capped, industry providers will need to adapt by diversifying their income streams beyond government funding and strengthening private sector partnerships, particularly as workforce gaps and rising demand for elder care add further strain. This is will likely culminate in revenue falling at an annualised 1.5% over the five years through 2030-31 to $2.3 billion.

The dataset consists of self-administered written texts by recruiters on how unemployed jobseeker status or periods of unemployment impact recruitment decisions. The data were collected for a Master's thesis. The writing invitation was aimed at individuals who had experience of recruiting employees as part of their jobs. In the writing guidelines, participants were instructed to write from the recruiter's point of view about the factors that need to be taken into consideration when recruiting unemployed or partially disabled individuals. The guidelines included questions on, for example, whether participants paid attention to periods of unemployment on applicants' CVs, the experiences participants had of assisted employment services, and what could increase interest in recruiting unemployed individuals. Background information included the participant's gender, age group, and the industry of employment for which the participant recruited employees. The data were organised into an easy to use HTML version at FSD. The dataset is available only in Finnish.

The Personal Welfare Services industry provides community and welfare services to disadvantaged individuals, including children, the elderly and Australians with long-term disabilities. The industry’s services include those designed to assist the frail and disabled in community settings, thereby circumventing the need for institutional care. As such it plays a key role in the wider care and support economy, one of Australia's fastest growing sectors and a key focus area of the current Labor Government. High and increasing government funding, including funding associated with the National Disability Insurance Scheme (NDIS), has benefited community and welfare service providers in Australia over the past decade. However, according to the Australian Council of Social Service, many of the social service organisations that deliver youth outreach services, disability support, and community legal services are now at a breaking point. This is because of unprecedented demand for their services following a series of external shocks in recent years – including bushfires, the COVID-19 pandemic and the cost-of-living crisis – with current funding levels proving inadequate to meet this additional demand. Inflationary cost pressures are adding to profit margin pressures and threatening the viability of several social assistance organisations. Industry revenue is expected to expand at an annualised 9.9% over the five years through 2024-25 to $43.2 billion. This rate includes anticipated growth of 6.8% in 2024-25 as a forecast rise in the unemployment rate, combined with high interest rates and the cost-of-living crisis, continues to take its toll, especially on disadvantaged Australians. However, funding shortfalls will constrain the industry’s ability to respond. Australia's ageing population will bolster demand for social assistance services in the coming years, particularly for in-home aged-care services. Simultaneously, new regulations governing the provision of aged care services and disability support services will influence the industry’s operating landscape. Changes to the industry's operating backdrop in view of the Government's care and support economy reform agenda will also shape the industry going forwards. Overall, industry revenue is forecast to climb by an annualised 5.8% over the five years through 2029-30, to $57.3 billion.

This table contains figures on the number of people with social security benefits per municipality, district and neighborhood (classification 2022). It concerns people with a benefit for disability, unemployment, old age and social assistance. It is possible for a person to claim more than one benefit. These may be benefits of the same type (for example, two benefits under the Disability Insurance Act (WAO)) or two benefits of different types (such as a benefit under the Unemployment Insurance Act and a social assistance benefit). In the latter case, the person is included in both types of benefits, in the first case only once (in the WAO). From October 2021, there will be an increase in the number of WGA benefits. The cause of this is an improvement in the quality of the process, which means that a group of self-insurers that were previously missing is now included. It is not about an increase in the regular number of WGA benefits, but an increase in "people with a WGA benefit". In the category of people on benefits (total), the person is of course only counted once. The figures on the number of people receiving benefits per neighbourhood, district or municipality may deviate slightly from figures published elsewhere on StatLine, because use is made of the most recent data from the Municipal Personal Records Database (BRP). Because different StatLine tables are updated at different times, it is possible that a different version of the BRP is used for one table than for another table. In that case, the most recently published figures are the most accurate. The figures relate to the last day of the reporting month. Data available from: March 2022. Status of the figures: The figures for 2022 are more provisional. Changes as of: 31 July 2023 Have been added - the more detailed provisional figures for December 2022 Have become more provisional - the figures for the period March, June and September 2022 When will new figures be released? These figures per municipality, district and neighborhood with a breakdown for 2022 appear in a new table.

ObjectiveWest Virginia’s (WV) suicide rate is 50% higher than the national average and is the highest in the Appalachian Region. Appalachia has several social factors that have contributed to greater socioeconomic deprivation, a known contributor of suicide. Given WV’s high prevalence of suicide and poverty, the current study aims to examine the relationship between socioeconomic deprivation and suicide rates in WV.MethodsThe Townsend Deprivation Index (TDI), Social Deprivation Index (SDI), and Social Vulnerability Index (SVI) measured socioeconomic deprivation. Negative binomial regression models assessed the relationship between socioeconomic deprivation scores, individual index items, and suicide rates. Model comparisons evaluated the indices’ ability to assess suicide rates. A backward selection strategy identified additional key items for examining suicide rates.ResultsThere was a significant increase in suicide rates for every 10% increase in TDI (β = 0.04; p < 0.01), SDI (β = 0.03; p = 0.04), and SVI scores (β = 0.05; p < 0.01). Household overcrowding and unemployment had a positive linear relationship with suicide in TDI (β = 0.04, p = 0.02; β = 0.07, p = 0.01), SDI (β = 0.10, p = 0.02; β = 0.01, p

This statistic shows the unemployment rate of Ukraine from 1999 to 2021. In 2021, the unemployment rate of Ukraine amounted to approximately 9.83 percent of the total labor force.

The economic situation in Ukraine

Amid the political and economic crisis, Ukraine’s unemployment is rising. When Russia seized and annexed Crimea in March of 2014, pro-Russian President Viktor Yanukovych was also ousted and the economy of Ukraine took a hard hit. This resulted in sharp reductions in Ukraine’s GDP, and likely caused a sharp increase in unemployment as well. Before the turmoil, Russia was Ukraine’s most important import and export partner, having a significant impact on GDP after tension arose. Meanwhile, Ukraine was and still is getting itself out of economic despair; Ukraine has amassed more debt with the IMF than Greece and is trying to reduce this debt by implementing hyper-austerity, which involves making cuts to public spending. Spending on unemployment and disability insurance is a part of these cuts, which is not ideal for the Ukrainian people considering that the unemployment rate is expected to reach a rate of 11.47 percent in 2015. In times of increasing unemployment, a struggling economy and an inflation rate reaching almost 50 percent, 2015 is and will be a tough, if not desastrous year for the Ukrainian people.